Run
10458923

Run 10458923

Task 9981 (Supervised Classification) cnae-9 Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.discr iminant_analysis.LinearDiscriminantAnalysis)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.049197275318980105
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth136
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features25
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1032
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.986969110457842
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.03355856323109527
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.19545254473694604
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.16107021747345823
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.02122547974190847
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1715
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components249
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.2631183964039193
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol4.4181983627125606e-07
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.9967 ± 0.0029
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9514 ± 0.0233
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9448 ± 0.0269
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9478 ± 0.026
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0114 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9509 ± 0.0239
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9524 ± 0.0191
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9509 ± 0.0239
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0578 ± 0.0274
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1013 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3222 ± 0.0722
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9509 ± 0.0239
Cross-validation details (10-fold Crossvalidation)